P253 Next-generation phenotyping facilitates the identification of structural brain malformations in rare disorders through computational brain MRI analysis by Tzung-Chien Hsieh, Shriya Jaddu, Hannah Weiland, Merle ten Hagen, Jing-Mei Li, Chi-Chia Chang, Sun-Yuan Hsieh, Hsin-Hung Chou, Gholson Lyon, William Dobyns, Wei-Liang Chen

“…To learn the brain structures from MRI, we applied transfer learning using ResNet-50, pre-trained on the fastMRI dataset from NYU School of Medicine, comprising 6,970 MRIs for age prediction. This model was then used to encode each MRI into a high-dimensional feature vector, creating the ''Clinical Brain Phenotype Space (CBPS).'' …”
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Machine learning-driven identification of key risk factors for predicting depression among nurses by Xiaoyan Qi, Xin Huang

“…We developed four predictive machine learning models: logistic regression, support vector machine, extreme gradient boosting machine (XGBoost), and adaptive boosting (AdaBoost). …”
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Neurocognition as a major predictor of 8-week response to antipsychotics for drug-naïve first-episode schizophrenia using machine learning by Xianghe Wang, Tianqi Gao, Xiaodong Guo, Bingjie Huang, Yunfei Ji, Wanheng Hu, Xiaolin Yin, Yue Zheng, Chengcheng Pu, Xin Yu

“…Six machine learning algorithms, including random forest, eXtreme gradient boosting (XGBoost), logistic regression, linear support vector machine (SVM), radial basis function SVM and poly SVM were applied and compared to draw the prediction model. …”
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Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon by Nicolás Amigo

“…This study integrated molecular dynamics (MD) simulations with machine learning techniques, specifically Linear, Ridge, and Support Vector Regression, to predict the thermodynamic properties of amorphous silicon (a-Si) under varying conditions. …”
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Robust-tuning machine learning algorithms for precise prediction of permeability impairment due to CaCO3 deposition by Mohammad Javad Khodabakhshi, Masoud Bijani, Masoud Hasani

“…The real value of this work lies in building a fine-tuned, practical machine learning approach that applies proven models to real-world EOR challenges. …”
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Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review by Khaldoon Alhusari, Salam Dhou

“…The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. …”
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Design and Motion Performance Analysis of 2URR-SRR-RUPUR Parallel Leg Rehabilitation Robot Mechanism by Zenglin Ye, Liang'an Zhang, Hua Chen, Hao Wu

“…The closed-loop vector method is used to solve the inverse kinematics of the mechanism and the velocity Jacobian matrix of the mechanism is analyzed. …”
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GLOBALIZATION OF SOCIETY AS ONE OF THE MAIN FACTORS OF THE NEW BUSINESS COMMUNICATION DISCOURSE FORMATION by Olena M. Turchak

“…Urgently, this issue manifests itself in the official business style, as the field of office work still uses a huge number of copies of the Russian language and forgets about the actual Ukrainian versions. …”
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Machine learning enhanced ultra-high vacuum system for predicting field emission performance in graphene reinforced aluminium based metal matrix composites by Sunil Kumar Pradhan, Subhayu Kabiraj, Shivin Kumar Gupta, Abhishek Singh, Padmakar G. Chavan, Shubham S. Patil, Trilok Nath Pandey

“…In Stage 1, datasets for pure aluminum, 0.5 wt% and 1.0 wt% graphene reinforced aluminium composites were used to train various ML models, categorized into five baskets: Decision tree-based, Support Vector models, Neural networks, Bayesian Models and Statistical Models. …”
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Resisting bad mouth attack in vehicular platoon using node-centric weight-based trust management algorithm (NC-WTM) by R. Priya, N. Sivakumar

“…Metrics, such as precision results of 78%, recall of 69.3%, F-score of 60.4%, and accuracy of 89%, are achieved and optimised in this trust model. Abbreviations: VANETs: vehicular ad hoc networks; IVC: inter-vehicular communication; NC-WTM: node-centric weight-based trust management algorithm; WTM: weight-based trust management algorithm; RPRep: robust and privacy-preserving reputation management scheme; ART: attack-resistant trust management scheme; MANET: mobile ad hoc network; DSRC: dedicated short-range communication; WAVE: wireless access in vehicular environment; IVC: inter-vehicular communication; I2V: infrastructure-to-vehicle; V2I: vehicle-to-infrastructure; V2V: vehicle-to-vehicle; TA: trust authority; RSU: road side unit; OBU: on-board unit; GPS: global positioning system; WSN: wireless sensor network; VASNETs: vehicular sensor networks; CCW: cooperative collision warning; BMA: bad mouth attack; TDMA: time division multiple access; GDVAN: greedy detection for VANETs; SMTS: spider monkey time synchronization; SVM: support vector machine; DST: Dempster-Shafer theory of evidence; TA: trust authority; PCA: puzzle-based co-authentication; VLC: visible light communication; NE: Nash equilibrium; RTB: request-to-broadcast; CTB: clear-to-broadcast; RREQ: route request message; RREP: route reply; DDR: data disseminate ratio; Dir: direct trust; IDir: indirect trust; TCE: trust computation error; PDR: packet delivery ratio…”
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Electromagnetic textile absorber applied to 4G and 5G bands by Paulo H. B. Carvalho, Ewaldo E. C. Santana, Allan K. D. Barros Filho, Paulo F. Silva Júnior, Keyll C. R. Martins, Talita C. Azevedo, Waldemir P. Martins, Mauro A. Medeiros, Rubens S. Gonçalves, Lourival M. Sousa Filho, Gricirene S. Correia, Gabrielle M. Fernandes, Talita C. Pinheiro, Brenda A. S. Rodrigues, Carlos A. M. Cruz

“…The measurements were carried out in a Vector Network Analyzer, model E5071C Agilent Technologies, with the characterization of the Denim substrate, the glue, and the identification of the best parameters for the construction of the absorber. …”
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Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…In a subsequent step, Machine Learning (ML) algorithms are employed to classify these tumors as malign or benign cases. A pre-trained model is developed to extract comprehensive features from colored mammography images by employing this approach. …”
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Macrophage Infiltration Correlated with IFI16, EGR1 and MX1 Expression in Renal Tubular Epithelial Cells Within Lupus Nephritis-Associated Tubulointerstitial Injury via Bioinformat... by Tian M, Tang M, Chen C, Lin Y, Chen H, Xu Y

“…Support vector machine-recursive feature elimination analysis and the least absolute shrinkage and selection operator regression model were used to screen for possible markers, and the compositional patterns of the 22 types of immune cell fractions in LN were determined using CIBERSORT. …”
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Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…The random forest regressor on the external dataset showed an MAE of 0.215 mSv and an MAPE of 7.1%. Conclusions: Our work demonstrated that machine learning models trained with data calculated by a dose-tracking software can provide good estimates of the effective dose just from patient and scanner parameters, without the need for a Monte Carlo approach.…”
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Performance of 2D Compressive Sensing on Wide-Beam Through-the-Wall Imaging by Edison Cristofani, Mathias Becquaert, Marijke Vandewal

“…Hardware simplicity, data, and measurement time reduction and simplified imagery are some of its most attractive strengths. This work aims at exploring the possibilities of using sparse vector recovery theory for actual engineering and defense- and security-oriented applications. …”
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REGULATION OF THE RELATIONSHIP BETWEEN ADDITIVE REDUCTION AND METRICS METHODS by E. M. Aristova

“…Objectives. The aim of the work is to determine the relationship between generalised criterion and target programming methods.Methods. …”
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Research and Design of Radiofrequency Antenna on LCD by Wanshan Zhu, Zhe Gao, Zhuo Meng, Chen Wang, Yang Li, Chunmei Wang, Yunwei Jia

“…This work studies the electromagnetic field of a radiofrequency (RF) antenna on 7-inch liquid crystal display (LCD) and presents a new approach where the RF antenna is designed on LCD. …”
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The analysis of artificial intelligence knowledge graphs for online music learning platform under deep learning by Shen Jiang, Ningning Shi, Chang Liu

“…Abstract This work proposes a personalized music learning platform model based on deep learning, aiming to provide efficient and customized learning recommendations by integrating audio, video, and user behavior data. …”
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Evaluating the Impact of Feature Engineering in Phishing URL Detection: A Comparative Study of URL, HTML, and Derived Features by Yanche Ari Kustiawan, Khairil Imran Ghauth

“…While many studies focus on either URL or HTML features, limited work has explored the comparative impact of engineered feature sets across different machine learning models. …”
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Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features by Ayodeji O. J. Ibitoye, Joseph D. Akinyemi, Olufade F. W. Onifade

“…These top-15 feature pairs were fed into five different ML models (decision tree (DT), neural networks (NN), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB)) for predicting the likelihood of diabetes, while also feeding the direct features (without correlated pairing) separately into the same 5[Formula: see text]ML models. …”
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